With the fast development of computer based vision system technology, the resolution, size and complexity of images becomes much greater and the tasks to which vision system technology are applied. Vision systems are used in widely diverse environments, such as military surveillance and character recognition.
Expert Systems based on sets of rules written by knowledgeable engineers based on interviews and observations of experts have been used to classify objects and anomalies in images. This time consuming method is very slow, inconsistent and difficult to adapt to new circumstances; results vary with the knowledge, training, ability, and fatigue of the operator.
One current technology is neural nets. This is limited by its requirement for a very large number of initial learning examples, usually several thousand, taking many hours to set up. Other technologies are also very complex computationally, requiring processor arrays specific to each equipment configuration at additional cost to meet performance requirements.
Other related technologies, such as convolution and correlation, cannot deal with variations in magnification, contrast, and rotation, which limits their utility to a very small range of constrained environments.